Capabilities

Bytelion offers a unique set of capabilities for applying data science to your business practices. Gain greater insights using contextual tools to visualize and analyze your data. Collaborate with others and share your insights via apps, reports, and dashboards

Data Integrity

Leverage your organization’s data experience with the right set of tools. Our quality assurance and data engineering teams will work tirelessly to ensure that you data sets will provide you with the answers you are looking and find new data sets when you need them.

Data Ecosystem Development

We use a variety of open source big data and analytics ecosystems. Some examples include:

  • Machine Learning
  • Custom Analytics in Python and R
  • Tensor Flow
  • Apache Hadoop
  • NoSQL/Hbase/Mongo/Hive
  • AWS

Extended AI Team

We meet your business needs using the best open source tools and techniques. Our seasoned data scientists can focus in on tackling many of the challenges needed to include:

  • Predictive Analytics
  • Social Media/Social Network Analysis
  • Behavior Analysis
  • Natural Language Processing

Our Process

Why is Data Science is one of the hottest trends in business?  There are numerous factors to include:  more data is now available, the tools and techniques for data science have matured, and most importantly, decision makers are more comfortable trusting data to help them make decisions.

While Data Science is the hot topic and everyone is hiring data scientists, there are a set of core problems that every company faces.  There is no silver bullet. Data Science (AI, deep learning, neural nets, machine learning etc) are not good at solving every problem. Companies need to stay incredibly focused on very specific issues to make an impact to their business operations.  One of the issues facing many companies is “Data Garbage In…”. In a recent Harvard Business Review article, only 3% of data was correct enough for managers to make decisions. Another issue that companies can struggle with is who and when to hire. Hiring four data scientists to solve a problem is most likely not going to be the answer.  It takes a disciplined, multi-skilled team to be the most impactful.

To combat common problems like data focus, poor datasets, and defining team roles, we developed the “Generis Flow Process” to streamline normal tasks into formulaic approach.  

Data Science Wheel
Bytelion Data Science Workflow

Frequently Asked Questions

What is the main pain this is going to solve for me?

Bytelion’s team can offload some of the other more pressing work for your team. By keeping them focused on what matters, you are giving your staff a chance to use the results from our modeling efforts.

How do I know if this is for me?

You will know this is for you if you meet our team and we can talk through some of your goals. There is always a chance that a service isn’t a good fit.  It is faster to determine this over a cup of coffee.

When should I implement this methodology?

The best time to implement this process is if and when you want to incorporate machine learning in one of your business processes.

OK, but how do I make sure I don’t over-engineer my applications?

Scope is always a HUGE problem.  The team has to feel comfortable with the results and being able to move forward.  Data isn’t perfect and neither are people, but a 95% answer where there was no answer is a great asset to have.

What type of organizations is this for?

This service works well for a small company that is singularly focused on a SAAS product built around AI or if you are a larger company and want to build an internal process to take advantage of your critical data.

How do you deliver it?

Our delivery teams work both off and onsite as needed.

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